Recommended prerequisite for participation in
the module
The module builds upon knowledge obtained in the modules Linear
algebra and Statistics
Content, progress and pedagogy of the
module
Cameras capture visual data from the surrounding world. Building
systems which can automatically process such data requires computer
vision methods. Students who complete the module will understand
the nature of digital images and video and have an inside into
relevant theories and methods within computer vision and an
understanding of their applicability.
Learning objectives
Knowledge
- Must have knowledge about the primary parameters of a camera
system
- Must have knowledge about the representation and compression of
digital images and video signal
- Must be able to understand the general framework of image
processing as well as the basic point and neighborhood operations,
i.e., binarization, color processing, BLOB analysis and
filtering
- Must be able to explain the principles behind invariant feature
point descriptors such as SIFT and Harris corners.
- Must have knowledge of different motion analysis methods, such
as background subtraction and optical flow
- Must be able to understand the tracking frameworks such as the
Kalman filter, mean-shift and the particle filter
- Must be able to understand different shape analysis methods
such as active-shape models, procrustes, Hungarian method
Skills
- Must be able to apply stereo vision to generate 3D date from
two or more cameras. This implies projective geometry, camera
calibration, epipolar geometry, correspondence and
triangulation
- Must be able to apply advanced 2D segmentation methods such as
Hough transform, compound morphology, and histogram-of-oriented
histograms.
- Must be able to demonstrate understanding of error propagation
techniques as a tool for performance characterization of computer
vision based solutions
Competences
- Must be able to learn further computer vision methods and
theories, and select an appropriate solution for a given
problem
Type of instruction
As described in ยง 17.
Exam
Exams
Name of exam | Image Processing and Computer Vision |
Type of exam | Written or oral exam |
ECTS | 5 |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |